It is known that engineered components may contain features (such as crystal phases in polycrystalline metallic components) or defects (such as slag particles in steel parts) which arise as a consequence of their manufacturing or fabrication.
Although characterisation of the bulk properties of the material can be performed, it can be difficult to quantify the direct effect of these defects on the mechanical properties of the component, particularly where the component is made from a composite material.
Large flaws in the component are likely to be easily spotted by visual or basic inspection or else are excluded through optimisation of the manufacturing process. In practice, this means that it is only the smaller flaws that present a concern. Depending on the type of component, the smaller flaws may also be effectively excluded (by being reduced to a statistically insignificant quantity) through careful management of the manufacturing process.
It is known to implement some type of quality assurance (QA) process in order to assess the quality of the manufacturing process and to determine the components' suitability for use. This process generally involves taking sample components from each manufacturing batch which are then sectioned in various orientations to check for voids or other flaws.
If a void or flaw is discovered in the sample, the entire batch of components from which the sample has been drawn will then be scrapped, or possibly reworked. This can result in the unnecessary scrapping or rework of components which may not exhibit the same actual flaws.
An alternative QA process involves the inspection of every item manufactured using a non-destructive technique, such as, for example, ultrasound or computer tomography (CT) methods. This will likely be more expensive to implement than the above-mentioned sampling method but may be appropriate for high value components or where manufacturing process stability is hard to achieve.
An example of a situation where manufacturing process stability can be problematic is the use of resin transfer moulding techniques where the resin itself has a finite shelf life. The shelf life reflects that fact that the resin is slowly curing from the point at which it is used for the first components in a batch and the end of the batch. As a result, the viscosity of the resin is slowly increasing which requires higher pressures to inject the resin into the component during its manufacture. This in turn can result in increasing levels of porosity from the first to the last components in a particular batch.
Consequently, a QA process in which resin injection pressure alone is monitored will not provide reliable indication of component quality and hence suitability for use.
Existing QA processes, whether destructive or non-destructive, generally involve ensuring that all flaws are below a threshold size or the total number of flaws (usually expressed in terms of the material's porosity) is below a limiting value. Both of these measures are generally impossible to measure directly.
The threshold size is generally set at a high level in order that it can be evaluated from flaws seen at the surface of the component.
Porosity on the other hand may be evaluated from pre-determined correlation with ultrasound absorption levels and can thus be inferred from ultrasound scanning of the component.
However, such an approach does not identify the distribution of flaws in the component, for example by identifying clusters of flaws which can make the component unserviceable.
FIGS. 1a to 1c show examples of the distribution of flaws in a material that might be considered to be acceptable by conventional “threshold size” or “total number” criteria, but would clearly not be acceptable in service.
FIG. 1a shows a material sample in which the flaws are aligned in a linear fashion across the material. This can result in cracks being likely to propagate between neighbouring flaws to produce a large crack and consequent fracture of the material.
FIG. 1b shows an arrangement where all the flaws in the material are clustered together in a single small region. This may have the effect that under any load conditions the material enclosed by the flaws is liable to crumble to create a single large flaw which in turn may result in structural failure of the material.
In FIG. 1c the flaws are present in one half of the material. This can result in the density of flaws (porosity) in that half of the material being approximately double the overall average flaw density. As a result, under uni-axial loading the material is liable to bend as well as extend.
One approach to assessing the distribution of flaws in a material to detect the presence of clustering would be to create an image of the material from a plurality of pixels and then to exhaustively examine each and every pixel, for example by using CT scanning techniques. This examination would indicate the presence of any flaw at a respective pixel.
A significant problem with the use of CT scanning techniques is that a single scan of a moderately sized component, such as a jet engine fan blade, can generate a vast quantity of data which in turn requires a huge image processing resource. This makes such techniques impractical for use as part of a routine QA process.
An exhaustive examination would examine every pixel (as shown in FIG. 2) and rank each one as either having “good” material properties or having “flawed” material properties. It would then be possible to determine a measure of overall porosity on the basis of the numbers of “good” and “flawed” pixels.
However in order to be able to determine average flaw size, variations in local porosity or a measure of the clustering of any flaws, it is necessary to store and interrogate both the material property and the physical position of each pixel in the material. Given the quantity of data which is likely to be generated from even a small material sample, this will be a computationally difficult task. Such analysis is likely therefore to be extremely time-consuming and expensive.
There is therefore a need for a more efficient technique for analysing a material with a view to providing a measure not only of the overall quantity of flaws in the material but also their size and distribution.